ArticleBMC nursing2026
Prevalence and characteristics of turnover intention among nurses in tertiary hospital: a national cross-sectional study.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
BACKGROUND AND
objectivesThe global shortage of nursing professionals is a pressing issue that requires immediate attention. Previous studies have shown that the turnover intention rate of nurses is high, which varies across different countries due to various influencing factors. This study aims to investigate the current state, characteristics, and associated factors of turnover intention among nurses in tertiary hospitals in China.
methodsThis is a large-sample cross-sectional study, and the included sample involved a total of 67 tertiary hospitals in 31 provinces in China. A validated questionnaire was used to measure the basic situation of nurses and the factors that may lead to nurses’ turnover intention. Data were analyzed using one-way analysis of variance and binary logistic regression.
resultsApproximately 13.7% of nurses expressed an intention to leave their positions, with the highest rates observed among ICU, pediatric, and emergency nurses. Univariate analysis showed that there were statistically significant differences in turnover intention among nurses based on GDP per capita, genders, ages, title, management positions, education levels, marital status, weekly working hours, night shift, and work satisfaction (P < 0.05). Furthermore, binary logistic regression analysis identified GDP per capita, age, gender, title, whether management positions, weekly working hours, night shift and work satisfaction as significant correlates of turnover intentions.
conclusionThis study reflects the turnover intention rates and characteristics of nurses in tertiary hospitals in China. Compared to other studies, the prevalence of turnover intention among nurses in China is relatively low. The identified characteristics of turnover intention provide insights for future research. These findings lay a foundation for future studies to explore the causal mechanisms in greater depth, thereby informing targeted interventions to improve nurse retention.
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